Developer Builds Quorel After Firecrawl Costs Proved Unsustainable for AI Agent Use

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A developer found that using Firecrawl as a data layer for an AI agent became costly and inefficient, as the tool re-fetched and re-charged for the same web pages each time fresh context was needed. The raw, unstructured scraper output also forced the agent to waste context window space parsing formatting rather than processing useful data. To solve this, the developer built Quorel, a tool that converts public websites into versioned, queryable APIs using plain-English schema descriptions and nightly automatic refreshes. Quorel maintains a history of changes through immutable versioned snapshots, allowing agents to detect what has changed over time without manual diffing. The tool includes a native MCP server on all plans, enabling agents to query only the specific, structured data they need rather than receiving entire page dumps.

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ProgrammingDEV Community ·

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